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Query Rewriting

20 articles · Page 1 of 1
Tech Ocean
Tech Ocean
Jun 9, 2026 · Artificial Intelligence

Spring AI Day 11: Advanced Modular RAG – Query Rewriting, Compression, and Empty‑Context Fallback

The article examines the limitations of a naïve QuestionAnswerAdvisor RAG implementation, introduces Spring AI's RetrievalAugmentationAdvisor for modular RAG, and demonstrates how query rewriting, multi‑turn compression, and an empty‑context augmenter improve recall and prevent model refusals.

Contextual AugmentationQuery RewritingRAG
0 likes · 6 min read
Spring AI Day 11: Advanced Modular RAG – Query Rewriting, Compression, and Empty‑Context Fallback
AI Engineer Programming
AI Engineer Programming
Jun 6, 2026 · Artificial Intelligence

How Query Rewriting Boosts Retrieval in RAG Systems

In RAG applications, ambiguous user queries often hinder retrieval effectiveness, so rewriting queries before search—through normalization, synonym expansion, linguistic rules, LLM‑based generation, query decomposition, and multi‑view strategies—can improve relevance, but must avoid over‑expansion, semantic drift, and added latency.

LLMPrompt EngineeringQuery Rewriting
0 likes · 11 min read
How Query Rewriting Boosts Retrieval in RAG Systems
Su San Talks Tech
Su San Talks Tech
Apr 19, 2026 · Artificial Intelligence

Boost Enterprise RAG: Data Pipeline Tricks, Hybrid Search & Rerank

To make Retrieval‑Augmented Generation reliable in production, the article outlines five key engineering tactics—semantic chunking with metadata, hybrid vector‑keyword search, two‑stage retrieval with reranking, query rewriting and expansion, and dynamic result evaluation—each illustrated with concrete examples and code snippets.

AI EngineeringHybrid SearchMetadata
0 likes · 10 min read
Boost Enterprise RAG: Data Pipeline Tricks, Hybrid Search & Rerank
Linyb Geek Road
Linyb Geek Road
Apr 17, 2026 · Artificial Intelligence

Bridging the Semantic Gap in RAG: Solving Mismatched Queries and Vector Store Answers

The article explains why RAG systems often retrieve irrelevant results due to a semantic gap between colloquial user questions and formal document language, and presents a four‑layer solution—including query rewriting, HyDE, multi‑query expansion, hierarchical indexing, hybrid search with RRF, rerankers, and embedding fine‑tuning—to systematically close that gap.

Document EnrichmentEmbedding Fine-tuningHybrid Search
0 likes · 14 min read
Bridging the Semantic Gap in RAG: Solving Mismatched Queries and Vector Store Answers
IT Services Circle
IT Services Circle
Apr 6, 2026 · Artificial Intelligence

Mastering RAG Interview Questions: A Complete Retrieval Optimization Blueprint

This article breaks down the full RAG retrieval pipeline—from query understanding and rewriting, through hybrid retrieval and reranking, to chunking, context compression, and dynamic routing—providing concrete techniques, formulas, and performance metrics to help candidates ace interview questions on RAG systems.

Context CompressionHard Negative MiningHybrid Retrieval
0 likes · 16 min read
Mastering RAG Interview Questions: A Complete Retrieval Optimization Blueprint
Alibaba International Intelligent Technology
Alibaba International Intelligent Technology
Nov 21, 2025 · Artificial Intelligence

How Large‑Model AI Transforms Multilingual E‑Commerce Search

Leveraging multilingual large‑language models such as Qwen, the authors reconstruct query understanding, product understanding, and relevance ranking for Alibaba’s international e‑commerce platforms, build the ME‑LLM base model with domain‑specific pre‑training, and demonstrate significant gains in semantic relevance, query rewriting, and downstream metrics like GMV.

ME-LLMQuery RewritingRL from search feedback
0 likes · 10 min read
How Large‑Model AI Transforms Multilingual E‑Commerce Search
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Oct 23, 2025 · Artificial Intelligence

Boost Your RAG Bot’s Accuracy: Hybrid Search, Query Rewriting, and Re‑ranking Explained

This article walks developers through three essential upgrades for Retrieval‑Augmented Generation systems—hybrid search combining vector and keyword retrieval, query rewriting to clarify conversational inputs, and re‑ranking with a cross‑encoder—providing step‑by‑step code examples using LangChain to dramatically improve answer quality.

AIHybrid SearchLangChain
0 likes · 9 min read
Boost Your RAG Bot’s Accuracy: Hybrid Search, Query Rewriting, and Re‑ranking Explained
Ubiquitous Tech
Ubiquitous Tech
Jun 6, 2025 · Artificial Intelligence

Part 5: Boosting Travel AI Chatbot with RAG – Anaphora Resolution and Query Rewriting

This article explains how to enhance a travel‑focused AI customer‑service system by importing structured and unstructured data into a RAG pipeline, then applying anaphora resolution and query rewriting techniques—illustrated with FastGPT workflows, prompt templates, and practical examples—to improve retrieval accuracy and answer relevance.

AI chatbotAnaphora ResolutionFastGPT
0 likes · 19 min read
Part 5: Boosting Travel AI Chatbot with RAG – Anaphora Resolution and Query Rewriting
DataFunTalk
DataFunTalk
Oct 16, 2022 · Artificial Intelligence

Query Understanding and Processing in E‑commerce Search Systems

This article explains the end‑to‑end pipeline of query understanding for e‑commerce search, covering preprocessing, segmentation, spell correction, normalization, and expansion, and discusses both academic research and industry implementations with examples and references.

Query ProcessingQuery Rewritingnatural language processing
0 likes · 13 min read
Query Understanding and Processing in E‑commerce Search Systems
Programmer DD
Programmer DD
Jul 15, 2022 · Databases

Boost MySQL Performance: 8 Common SQL Pitfalls and How to Fix Them

This article examines frequent MySQL performance problems—such as inefficient LIMIT usage, implicit type conversion, suboptimal joins, mixed ordering, EXISTS subqueries, condition pushdown limitations, early range reduction, and intermediate result handling—and provides concrete rewrites and execution‑plan analyses that dramatically improve query speed.

MySQLQuery RewritingSQL Optimization
0 likes · 16 min read
Boost MySQL Performance: 8 Common SQL Pitfalls and How to Fix Them
Meituan Technology Team
Meituan Technology Team
Feb 17, 2022 · Artificial Intelligence

Query Rewriting in Meituan Search: Iterative Directions and System Design

The paper presents Meituan’s end‑to‑end query‑rewriting system—combining large‑scale corpus mining, BERT‑based semantic discrimination, and multiple online services such as dictionary, SMT/NMT, and vector retrieval—to bridge semantic gaps, boost recall, now serving roughly 73% of search traffic at up to 60 k QPS, with future work targeting deeper vector methods and generative rewriting.

MeituanQuery Rewriting
0 likes · 42 min read
Query Rewriting in Meituan Search: Iterative Directions and System Design
58 Tech
58 Tech
Nov 18, 2021 · Artificial Intelligence

Intelligent Search Strategy for 58 Recruitment: Breaking Category Constraints and Building a Smart Recall Framework

This article describes how 58 recruitment revamped its search system by removing rigid category limits, introducing query rewriting, intent recognition, doc understanding, and vector‑based recall, resulting in significantly higher relevance, reduced bad cases, and improved commercial performance.

AIQuery RewritingRecruitment
0 likes · 14 min read
Intelligent Search Strategy for 58 Recruitment: Breaking Category Constraints and Building a Smart Recall Framework
DataFunSummit
DataFunSummit
Jul 25, 2021 · Artificial Intelligence

Advances in Query Understanding and Semantic Retrieval at Zhihu Search

This article details Zhihu Search's engineering solutions for long‑tail query challenges, covering historical development, term weighting, synonym expansion, query rewriting with reinforcement learning, and semantic recall using BERT‑based models, while also outlining future research directions such as GAN‑based rewriting and lightweight pre‑training.

BERTEmbedding RetrievalQuery Rewriting
0 likes · 14 min read
Advances in Query Understanding and Semantic Retrieval at Zhihu Search
DataFunTalk
DataFunTalk
Mar 9, 2020 · Artificial Intelligence

Microsoft XiaoIce: Architecture and Techniques for Persona‑Driven Conversational AI

The article presents an in‑depth overview of Microsoft XiaoIce’s conversational AI platform, covering system classification, the three major dialogue directions, long‑term conversation handling, query understanding and rewriting, few‑shot learning with prototypical networks, retrieval and generation models, empathy modeling, and the concept of AI beings for building personalized chatbots.

AI beingsConversational AIDialogue Systems
0 likes · 26 min read
Microsoft XiaoIce: Architecture and Techniques for Persona‑Driven Conversational AI
ITPUB
ITPUB
Feb 12, 2020 · Databases

8 MySQL Tricks to Supercharge Query Performance and Slash Execution Time

This article examines common MySQL performance pitfalls—such as large OFFSET limits, implicit type conversion, inefficient UPDATE/DELETE joins, mixed ordering, EXISTS subqueries, and poor condition push‑down—and demonstrates concrete query rewrites that reduce execution times from seconds to milliseconds.

DatabasesMySQLQuery Rewriting
0 likes · 7 min read
8 MySQL Tricks to Supercharge Query Performance and Slash Execution Time
DataFunTalk
DataFunTalk
Jan 2, 2020 · Artificial Intelligence

Improving Zhihu Search: Query Understanding, Term Weighting, Synonym Expansion, Query Rewriting, and Semantic Retrieval

This article details Zhihu's search engineering advances over the past year, covering long‑tail query challenges, term‑weight calculation, synonym expansion, query rewriting with translation models and reinforcement learning, and semantic retrieval using BERT‑based embeddings, while outlining future research directions.

NLPQuery Rewritingquery understanding
0 likes · 14 min read
Improving Zhihu Search: Query Understanding, Term Weighting, Synonym Expansion, Query Rewriting, and Semantic Retrieval
Amap Tech
Amap Tech
Nov 21, 2019 · Artificial Intelligence

Advances in Geographic Text Processing for Map Search: Query Analysis, Error Correction, Rewriting, and Omission

Recent advances in map‑search text processing replace rule‑based pipelines with machine‑learning and deep‑learning models for query analysis, error correction, rewriting, and omission, using phonetic and spatial entity correction, vector‑based similarity, and CRF sequence labeling within a three‑stage architecture of analysis, recall, and ranking to deliver more precise POI results.

Error CorrectionMap SearchNLP
0 likes · 17 min read
Advances in Geographic Text Processing for Map Search: Query Analysis, Error Correction, Rewriting, and Omission
Architect
Architect
Apr 23, 2016 · Artificial Intelligence

Architecture and Techniques of an E‑commerce Search Engine

The article explains the overall architecture of an e‑commerce search engine, covering indexing, static scoring, retrieval, title and store deduplication, query analysis and rewriting, and related big‑data and AI techniques used to improve relevance and diversity of search results.

Query RewritingRankingdeduplication
0 likes · 14 min read
Architecture and Techniques of an E‑commerce Search Engine